Search Results - (( pattern bees algorithm ) OR ( patterns ((mining algorithm) OR (_ algorithm)) ))

Data normalization techniques in swarm-based forecasting models for energy commodity spot price by Yusof, Yuhanis, Mustaffa, Zuriani, Kamaruddin, Siti Sakira

“…Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study investigates the effect of different data normalization techniques.which are Min-max, Z-score and decimal scaling, on Swarm-based forecasting models.Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC).Forecasting models are later developed to predict the daily spot price of crude oil and gasoline.Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max.Nevertheless, the GWO is more superior than ABC as its model generates the highest accuracy for both crude oil and gasoline price.Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.…”
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Neural Network Training Using Hybrid Particle-move Artificial Bee Colony Algorithm for Pattern Classification by Nuaimi, Zakaria Noor Aldeen Mahmood Al, Abdullah, Rosni

“…Artificial Bees Colony (ABC) optimization algorithm is one of the competitive algorithms in the SI algorithms group. …”
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Neural network training using hybrid particle-move artificial bee colony algorithm for pattern classification by Al Nuaimi, Zakaria Noor Aldeen Mahmood, Abdullah, Rosni

“…In this work, we aimed to highlight the performance of the Hybrid Particle-move Artificial Bee Colony (HPABC) algorithm by applying it on the ANNT application.The performance of the HPABC algorithm was investigated on four benchmark pattern-classification data sets and the results were compared with other algorithms.The results obtained illustrate that HPABC algorithm can efficiently be used for ANNT.HPABC outperformed the original ABC and PSO as well as other state-of-art and hybrid algorithms in terms of time, function evaluation number and recognition accuracy.…”
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Application of the bees algorithm to the selection features for manufacturing data by Pham, D.T, Mahmuddin, Massudi, Otri, S., Al-Jabbouli, H.

“…The Bees Algorithm is employed to select an optimal set of features for a particular pattern classification task. …”
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Modifying iEclat algorithm for infrequent patterns mining by Julaily Aida, J., Mustafa, M.

“…This paper proposes an enhancement algorithm based on iEclat algorithms for mining infrequent pattern.…”
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Modifying iEclat algo ithm for infrequent patterns mining by Julaily Aida, Jusoh, Mustafa, Man

“…This paper proposes an enhancement algorithm based on iEclat algorithms for mining infrequent pattern.…”
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Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset by Julaily Aida, Jusoh, Wan Aezwani, Wan Abu Bakar, Mustafa, Man

“…The multiple variants in the R-Eclat algorithm generate varied performances in infrequent mining patterns. …”
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Frequent Lexicographic Algorithm for Mining Association Rules by Mustapha, Norwati

“…An algorithm called Flex (Frequent lexicographic patterns) has been proposed in obtaining a good performance of searching li-equent patterns. …”
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Algorithms for frequent itemset mining: a literature review by Chee, C.-H., Jaafar, J., Aziz, I.A., Hasan, M.H., Yeoh, W.

“…This paper reviews and presents a comparison of different algorithms for Frequent Pattern Mining (FPM) so that a more efficient FPM algorithm can be developed. …”
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Clustering natural language morphemes from EEG signals using the Artificial Bee Colony algorithm by Sulaiman, Suriani, Ahmed Yahya, Saba, Mohd Shukor, Nur Sakinah, Ismail , Amelia Ritahani, Zaahirah, Qazi, Yaacob, Hamwira, Abdul Rahman, Abdul Wahab, Dzulkifli, Mariam Adawiah

“…This study aims at analyzing EEG signals for the purpose of clustering natural language morphemes using the Artificial Bee Colony (ABC) algorithm. Using as input the features extracted from EEG signals during morphological priming tasks, our experimental results indicate that applying the ABC algorithm on EEG datasets to cluster Malay morphemes produces promising results.…”
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DFP-growth: An efficient algorithm for mining frequent patterns in dynamic database by Zailani, Abdullah, Herawan, Tutut, Noraziah, Ahmad, Mustafa, Mat Deris

“…Nowadays, FP-Growth is one of the famous and benchmarked algorithms to mine the frequent patterns from FP-Tree data structure. …”
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Web user navigation pattern mining approach based on graph partitioning algorithm. by Jalali, Mehrdad, Mustapha, Norwati, Mamat, Ali, Sulaiman, Md. Nasir

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Efficient prime-based method for interactive mining of frequent patterns. by Mohammad Hossein, Nadimi Shahraki, Mustapha, Norwati, Sulaiman, Md. Nasir, Mamat, Ali

“…Since rerunning mining algorithms from scratch is very costly and time-consuming, researchers have introduced interactive mining of frequent patterns. …”
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Prime-based method for interactive mining of frequent patterns by Nadimi-Shahraki, Mohammad-Hossein

“…Since rerunning the mining algorithms from scratch can be very time consuming, researchers have introduced interactive mining to find proper patterns by using the current mining model with various minsup. …”
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Clustering natural language morphemes from EEG signals using the Artificial Bee Colony algorithm by Sulaiman, Suriani, Ahmed Yahya, Saba, Mohd Shukor, Nur Sakinah, Ismail , Amelia Ritahani, Zaahirah, Qazi, Yaacob, Hamwira Sakti, Abdul Rahman, Abdul Wahab, Dzulkifli, Mariam Adawiah

“…This study aims at analyzing EEG signals for the purpose of clustering natural language morphemes using the Artificial Bee Colony (ABC) algorithm. Using as input the features extracted from EEG signals during morphological priming tasks, our experimental results indicate that applying the ABC algorithm on EEG datasets to cluster Malay morphemes produces promising results.…”
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Expectation maximization clustering algorithm for user modeling in web usage mining system by Mustapha, Norwati, Jalali, Manijeh, Jalali, Mehrdad

“…The results also indicate that kind of behavior given by EM clustering algorithm has improved the visit-coherence (accuracy) of navigation pattern mining.…”
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A frequent pattern mining algorithm based on FP-growth without generating tree by Tohidi, Hossein, Ibrahim, Hamidah

“…Our algorithm works based on prime factorization, and is called Frequent Pattern-Prime Factorization (FPPF).…”
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A frequent pattern mining algorithm based on FP-growth without generating tree by Tohid, Hossein, Ibrahim, Hamidah

“…It then divides the compressed database into a set of conditional databases (a special kind of projected database), each associated with one frequent item or pattern fragment, and mines each such database separately.For a large database, constructing a large tree in the memory is a time consuming task and increase the time of execution.In this paper we introduce an algorithm to generate frequent patterns without generating a tree and therefore improve the time complexity and memory complexity as well.Our algorithm works based on prime factorization, and is called Frequent Pattern- Prime Factorization (FPPF).…”
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A numerical method for frequent pattern mining by Mustapha, Norwati, Nadimi-Shahraki, Mohammad-Hossein, Mamat, Ali, Sulaiman, Md. Nasir

“…Frequent pattern mining is one of the active research themes in data mining. …”
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IncSPADE: An Incremental Sequential Pattern Mining Algorithm Based on SPADE Property by Omer, Adam, Zailani, Abdullah, Amir, Ngah, Kasypi, Mokhtar, Wan Muhamad Amir, Wan Ahmad, Herawan, Tutut, Noraziah, Ahmad, Mustafa, Mat Deris, Abdul Razak, Hamdan

“…In this paper we propose Incremental Sequential PAttern Discovery using Equivalence classes (IncSPADE) algorithm to mine the dynamic database without the requirement of re-scanning the database again. …”
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